Development and evaluation of four PET image-based dual respiratory and cardiac motion estimation methods

摘要

We have previously shown 4D image reconstructions with motion compensation using accurate model of dual respiratory and cardiac (R&C) motions provides much improved 4D cardiac gated image qualities. The goal of this study is to develop and evaluate 4 R&C motion vector field (MVF) estimation methods based on the improved 4D PET images. In Method 1, the dual R&C motions are estimated directly from the dual R&C gated images. In Methods 2, 3 and 4, they are estimated indirectly by estimating the respiratory motion (RM) and cardiac motion (CM) separately from the respiratory gated only and cardiac gated only images. Methods also models the effects of RM on CM estimation by applying an image-based RM correction on the cardiac gated images while Methods 4 iteratively models the mutual effects of RM and CM estimations. Realistic and almost noise-free PET projection data were generated from the 4D XCAT phantom with realistic and known R&C MVF using Monte Carlo simulation. They were subsequently scaled and were added Poisson noise to generate additional datasets with 2 more different noise levels, and were reconstructed using a 4D image reconstruction method to obtain dual R&C gated images. The four dual R&C MVF estimation methods were applied to the dual R&C gated images and the estimated MVFs were compared to the known R&C MVFs. The resultant MVFs show that among the 4 estimation methods, Methods 2 performed the worst for noise-free case while Method 1 performed the worst for noisy cases in terms of the average mean-squared-errors (MSEs) between estimated and known MVFs. Methods 4 and 3 showed comparable results and provide reduced MSE by up to 35% of that in Method 1 for noisy cases. We have developed and evaluated 4 different R&C MVF estimation methods for use in 4D PET image reconstruction with accurate motion correction and found separate R&C estimation with modeling of RM on CM estimation (Method 3) to be the best option for accurate estimation of dual R&C motion.